Project Summary/Abstract Numerous evidence-based behavioral and pharmacological interventions have been developed and some have been adopted by alcohol treatment providers, however very little is known why specific treatments are effective (i.e., what are the mechanisms of behavior change in treatment) and which treatments are most effective for specific individuals. Despite an increased focus on mechanisms of behavior change among alcohol researchers over the past 15 years, many findings have not been replicated and an evidence-based understanding of why and for whom individual treatments for alcohol use disorder (AUD) are effective remains elusive. First, it is clear that AUD is heterogeneous in both etiology and outcomes following treatment, yet heterogeneity in mediators of AUD treatment has not been considered, mostly because of methodological limitations. In general, the mechanisms of behavior change following alcohol treatment are not well understood. Second, AUD treatment studies investigate multiple mechanisms of behavior change. One issue is a lack of understanding and methodological techniques to test which mechanism of behavior change is robust if it were to be replicated under a different set of conditions (e.g., different participants with different mechanisms of behavior change). To address these gaps in understanding, this study will build an empirical knowledge base by identifying robust mechanisms of behavior change (i.e., do mechanisms replicate across different participants?) and by modeling heterogeneity in alcohol treatment (i.e., for whom it works/fails to work) through development and application of the state-of-the-art methodological techniques, extensive analyses of eleven existing randomized clinical trials for AUD, as well as simulation studies based on AUD clinical trial data. Candidate mechanisms of behavior change, based on prior studies and the dynamic model of relapse as a guiding theoretical model, will include self-efficacy, craving or urges to drink, mutual help involvement, social support, therapeutic alliance, client language, and coping responses. Results from the proposed study will inform an individualized approach to formulating treatment recommendations, clarify which treatment mechanisms (mediators) are most robust across studies, and directly inform clinical decision making. In addition, the proposed study will provide an extensive suite of data analytic tools that will be designed specifically to answer questions regarding (a) testing robustness of candidate mediators, and (b) testing mediation in the presence of heterogeneity. The analytic tools will accommodate features of data commonly observed in AUD clinical trials and will be accessible to substantive researchers with knowledge of regression and mediation. The ultimate goal of this study is to yield a better understanding of mechanisms of AUD treatment outcomes in order to inform precision medicine initiatives.